Abstract:

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Impulse response provides important information about flaws in mechanical system.
Deconvolution is one system identification technique for fault detection when signals captured from
bearings with and without flaw are both available. However effects of measurement systems and
noise are obstacles to the technique. In the present study, a model, namely autoregressive-moving
average (ARMA), is used to estimate vibration pattern of rolling element bearings for fault
detection. The frequently used ARMA estimator cannot characterize non-Gaussian noise
completely. Aimed at circumventing the inefficiency of the second-order statistics-based ARMA
estimator, higher-order statistics (HOS) was introduced to ARMA estimator, which eliminates the
effect of noise greatly and, therefore, offers more accurate estimation of the system. Furthermore,
bispectrums of the estimated HOS-based ARMA models were subsequently applied to get clearer
information. Impulse responses of signals captured from the test bearings without and with flaws
and their bispectra were compared for the purpose of fault detection. The results demonstrated the
excellent capability of this method in vibration signal processing and fault detection.

Abstract: For the time varying of signals, empirical mode decomposition (EMD) is occupied to modulate signals; auto-regressive moving average (ARMA) of higher accuracy is used to establish model for the signal principal components; then parametric bi-cepstrum estimation is implemented and fault feature is extracted. The test results about gearbox of overhead traveling crane indicate: the feature quefrency can be obtained through method of EMD and ARMA model parametric bi-cepstrum estimation.It is a kind of effective fault diagnosis and stability evaluation method.

Abstract: In this paper, a fast algorithm for vector autoregressivemoving-average (ARMA) parameter estimation under noise environments is proposed. Based on an equivalent AR parameter model technique and a Yule-Walker equation technique, solving the parameter estimation problem of the VARMA model is well converted into solving linear equations. Therefore, the proposed algorithm has a lower computational complexity and a faster speed than conventional algorithms. Application examples with application to Lorenz systems confirm that the proposed algorithm can obtain a good solution.

Abstract: This paper focus on rotor-bearing system parameter identification with impulse excitation in horizon and vertical which is based on Backward -auto-regressive model. Singular value decomposition is applied to reduce the noise and the proper AR model order and de-noising threshold are selected. In this paper, the damping ratio is identified within the different rotating speed and different impulse excitation, and the error is calculated within the different noise level and different AR model order when compared with the ideal model. Though the theoretical analysis, simulation analysis and experimental research, We can indicate that the BAR model has a good performance in system identification and elimination of false modal.

Abstract: Due to the interference of strong vibration signal, a big error exists in modal parameter identification based on traditional ARMA model. In order to solve this problem, a new method for modal parameter identification based on natural excitation technique (NExT) and ARMA model is presented. Firstly, NExT is used to obtain cross-correlation function of different response sequences. Then, ARMA model of cross-correlation function is established, and the parameters of ARMA model are estimated by least squares method. Finally, the modal parameter identification is achieved using the relationship between the ARMA parameters and structural modal parameters. Experiment shows that the identification results of our method are more accurate than the traditional ARMA model.